2 citations · 4 across the 6 of their papers we have counts for
6 papers
Causal Inference with Complex Treatments: A Survey
Yingrong Wang, Haoxuan Li, Minqin Zhu +4
Causal inference plays an important role in explanatory analysis and decision making across various fields like statistics, marketing, health care, and education. Its main task is…
Stable Heterogeneous Treatment Effect Estimation across Out-of-Distribution Populations
Yuling Zhang, Anpeng Wu, Kun Kuang +3
Heterogeneous treatment effect (HTE) estimation is vital for understanding the change of treatment effect across individuals or subgroups. Most existing HTE estimation methods focu…
Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimation
Minqin Zhu, Anpeng Wu, Haoxuan Li +8
Estimating the individuals' potential response to varying treatment doses is crucial for decision-making in areas such as precision medicine and management science. Most recent stu…
Pareto-Optimal Estimation and Policy Learning on Short-term and Long-term Treatment Effects
Yingrong Wang, Anpeng Wu, Haoxuan Li +5
This paper focuses on developing Pareto-optimal estimation and policy learning to identify the most effective treatment that maximizes the total reward from both short-term and lon…
Hierarchical Topological Ordering with Conditional Independence Test for Limited Time Series
Anpeng Wu, Haoxuan Li, Kun Kuang +2
Learning directed acyclic graphs (DAGs) to identify causal relations underlying observational data is crucial but also poses significant challenges. Recently, topology-based method…
Learning Instrumental Variable from Data Fusion for Treatment Effect Estimation
Anpeng Wu, Kun Kuang, Ruoxuan Xiong +6
The advent of the big data era brought new opportunities and challenges to draw treatment effect in data fusion, that is, a mixed dataset collected from multiple sources (each sour…